# Text-to-image

<div class="flex flex-wrap space-x-1">
  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>

The Stable Diffusion model was created by researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [Runway](https://github.com/runwayml), and [LAION](https://laion.ai/). The [StableDiffusionPipeline](/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline) is capable of generating photorealistic images given any text input. It's trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs. Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.

The abstract from the paper is:

*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at https://github.com/CompVis/latent-diffusion.*

> [!TIP]
> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
>
> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!

## StableDiffusionPipeline[[diffusers.StableDiffusionPipeline]]

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<docstring><name>class diffusers.StableDiffusionPipeline</name><anchor>diffusers.StableDiffusionPipeline</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L154</source><parameters>[{"name": "vae", "val": ": AutoencoderKL"}, {"name": "text_encoder", "val": ": CLIPTextModel"}, {"name": "tokenizer", "val": ": CLIPTokenizer"}, {"name": "unet", "val": ": UNet2DConditionModel"}, {"name": "scheduler", "val": ": KarrasDiffusionSchedulers"}, {"name": "safety_checker", "val": ": StableDiffusionSafetyChecker"}, {"name": "feature_extractor", "val": ": CLIPImageProcessor"}, {"name": "image_encoder", "val": ": CLIPVisionModelWithProjection = None"}, {"name": "requires_safety_checker", "val": ": bool = True"}]</parameters><paramsdesc>- **vae** ([AutoencoderKL](/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) --
  Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
- **text_encoder** ([CLIPTextModel](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel)) --
  Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
- **tokenizer** ([CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer)) --
  A `CLIPTokenizer` to tokenize text.
- **unet** ([UNet2DConditionModel](/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel)) --
  A `UNet2DConditionModel` to denoise the encoded image latents.
- **scheduler** ([SchedulerMixin](/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin)) --
  A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
  [DDIMScheduler](/docs/diffusers/main/en/api/schedulers/ddim#diffusers.DDIMScheduler), [LMSDiscreteScheduler](/docs/diffusers/main/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler), or [PNDMScheduler](/docs/diffusers/main/en/api/schedulers/pndm#diffusers.PNDMScheduler).
- **safety_checker** (`StableDiffusionSafetyChecker`) --
  Classification module that estimates whether generated images could be considered offensive or harmful.
  Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
  more details about a model's potential harms.
- **feature_extractor** ([CLIPImageProcessor](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor)) --
  A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.</paramsdesc><paramgroups>0</paramgroups></docstring>

Pipeline for text-to-image generation using Stable Diffusion.

This model inherits from [DiffusionPipeline](/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).

The pipeline also inherits the following loading methods:
- [load_textual_inversion()](/docs/diffusers/main/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion) for loading textual inversion embeddings
- [load_lora_weights()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights) for loading LoRA weights
- [save_lora_weights()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights) for saving LoRA weights
- [from_single_file()](/docs/diffusers/main/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file) for loading `.ckpt` files
- [load_ip_adapter()](/docs/diffusers/main/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter) for loading IP Adapters





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<docstring><name>__call__</name><anchor>diffusers.StableDiffusionPipeline.__call__</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L778</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- **height** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The height in pixels of the generated image.
- **width** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The width in pixels of the generated image.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **timesteps** (`List[int]`, *optional*) --
  Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  A higher guidance scale value encourages the model to generate images closely linked to the text
  `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide what to not include in image generation. If not defined, you need to
  pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
  applies to the [DDIMScheduler](/docs/diffusers/main/en/api/schedulers/ddim#diffusers.DDIMScheduler), and is ignored in other schedulers.
- **generator** (`torch.Generator` or `List[torch.Generator]`, *optional*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
  generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
  not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a [StableDiffusionPipelineOutput](/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in
  [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **guidance_rescale** (`float`, *optional*, defaults to 0.0) --
  Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
  using zero terminal SNR.
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`List`, *optional*) --
  The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
  will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
  `._callback_tensor_inputs` attribute of your pipeline class.</paramsdesc><paramgroups>0</paramgroups><rettype>[StableDiffusionPipelineOutput](/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) or `tuple`</rettype><retdesc>If `return_dict` is `True`, [StableDiffusionPipelineOutput](/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.</retdesc></docstring>

The call function to the pipeline for generation.



<ExampleCodeBlock anchor="diffusers.StableDiffusionPipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline

>>> pipe = StableDiffusionPipeline.from_pretrained(
...     "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")

>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt).images[0]
```

</ExampleCodeBlock>







</div>
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<docstring><name>enable_attention_slicing</name><anchor>diffusers.StableDiffusionPipeline.enable_attention_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1978</source><parameters>[{"name": "slice_size", "val": ": typing.Union[int, str, NoneType] = 'auto'"}]</parameters><paramsdesc>- **slice_size** (`str` or `int`, *optional*, defaults to `"auto"`) --
  When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
  `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
  provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
  must be a multiple of `slice_size`.</paramsdesc><paramgroups>0</paramgroups></docstring>

Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
in slices to compute attention in several steps. For more than one attention head, the computation is performed
sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.

> [!WARNING] > ⚠️ Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA)
from PyTorch > 2.0 or xFormers. These attention computations are already very memory efficient so you won't
need to enable > this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious
slow downs!



<ExampleCodeBlock anchor="diffusers.StableDiffusionPipeline.enable_attention_slicing.example">

Examples:

```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline

>>> pipe = StableDiffusionPipeline.from_pretrained(
...     "stable-diffusion-v1-5/stable-diffusion-v1-5",
...     torch_dtype=torch.float16,
...     use_safetensors=True,
... )

>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> pipe.enable_attention_slicing()
>>> image = pipe(prompt).images[0]
```

</ExampleCodeBlock>


</div>
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<docstring><name>disable_attention_slicing</name><anchor>diffusers.StableDiffusionPipeline.disable_attention_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L2015</source><parameters>[]</parameters></docstring>

Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is
computed in one step.


</div>
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<docstring><name>enable_vae_slicing</name><anchor>diffusers.StableDiffusionPipeline.enable_vae_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L2180</source><parameters>[]</parameters></docstring>

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.


</div>
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<docstring><name>disable_vae_slicing</name><anchor>diffusers.StableDiffusionPipeline.disable_vae_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L2193</source><parameters>[]</parameters></docstring>

Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.


</div>
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<docstring><name>enable_xformers_memory_efficient_attention</name><anchor>diffusers.StableDiffusionPipeline.enable_xformers_memory_efficient_attention</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1921</source><parameters>[{"name": "attention_op", "val": ": typing.Optional[typing.Callable] = None"}]</parameters><paramsdesc>- **attention_op** (`Callable`, *optional*) --
  Override the default `None` operator for use as `op` argument to the
  [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
  function of xFormers.</paramsdesc><paramgroups>0</paramgroups></docstring>

Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this
option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
up during training is not guaranteed.

> [!WARNING] > ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient
attention takes > precedent.



<ExampleCodeBlock anchor="diffusers.StableDiffusionPipeline.enable_xformers_memory_efficient_attention.example">

Examples:

```py
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

>>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
>>> # Workaround for not accepting attention shape using VAE for Flash Attention
>>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)
```

</ExampleCodeBlock>


</div>
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<docstring><name>disable_xformers_memory_efficient_attention</name><anchor>diffusers.StableDiffusionPipeline.disable_xformers_memory_efficient_attention</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1952</source><parameters>[]</parameters></docstring>

Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).


</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>enable_vae_tiling</name><anchor>diffusers.StableDiffusionPipeline.enable_vae_tiling</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L2206</source><parameters>[]</parameters></docstring>

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.


</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>disable_vae_tiling</name><anchor>diffusers.StableDiffusionPipeline.disable_vae_tiling</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L2220</source><parameters>[]</parameters></docstring>

Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.


</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>load_textual_inversion</name><anchor>diffusers.StableDiffusionPipeline.load_textual_inversion</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/textual_inversion.py#L263</source><parameters>[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]]"}, {"name": "token", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "tokenizer", "val": ": typing.Optional[ForwardRef('PreTrainedTokenizer')] = None"}, {"name": "text_encoder", "val": ": typing.Optional[ForwardRef('PreTrainedModel')] = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **pretrained_model_name_or_path** (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`) --
  Can be either one of the following or a list of them:

  - A string, the *model id* (for example `sd-concepts-library/low-poly-hd-logos-icons`) of a
    pretrained model hosted on the Hub.
  - A path to a *directory* (for example `./my_text_inversion_directory/`) containing the textual
    inversion weights.
  - A path to a *file* (for example `./my_text_inversions.pt`) containing textual inversion weights.
  - A [torch state
    dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

- **token** (`str` or `List[str]`, *optional*) --
  Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a
  list, then `token` must also be a list of equal length.
- **text_encoder** ([CLIPTextModel](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel), *optional*) --
  Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
  If not specified, function will take self.tokenizer.
- **tokenizer** ([CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer), *optional*) --
  A `CLIPTokenizer` to tokenize text. If not specified, function will take self.tokenizer.
- **weight_name** (`str`, *optional*) --
  Name of a custom weight file. This should be used when:

  - The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight
    name such as `text_inv.bin`.
  - The saved textual inversion file is in the Automatic1111 format.
- **cache_dir** (`Union[str, os.PathLike]`, *optional*) --
  Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
  is not used.
- **force_download** (`bool`, *optional*, defaults to `False`) --
  Whether or not to force the (re-)download of the model weights and configuration files, overriding the
  cached versions if they exist.

- **proxies** (`Dict[str, str]`, *optional*) --
  A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
  'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- **local_files_only** (`bool`, *optional*, defaults to `False`) --
  Whether to only load local model weights and configuration files or not. If set to `True`, the model
  won't be downloaded from the Hub.
- **hf_token** (`str` or *bool*, *optional*) --
  The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
  `diffusers-cli login` (stored in `~/.huggingface`) is used.
- **revision** (`str`, *optional*, defaults to `"main"`) --
  The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
  allowed by Git.
- **subfolder** (`str`, *optional*, defaults to `""`) --
  The subfolder location of a model file within a larger model repository on the Hub or locally.
- **mirror** (`str`, *optional*) --
  Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
  guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
  information.</paramsdesc><paramgroups>0</paramgroups></docstring>

Load Textual Inversion embeddings into the text encoder of [StableDiffusionPipeline](/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline) (both 🤗 Diffusers and
Automatic1111 formats are supported).



Example:

<ExampleCodeBlock anchor="diffusers.StableDiffusionPipeline.load_textual_inversion.example">

To load a Textual Inversion embedding vector in 🤗 Diffusers format:

```py
from diffusers import StableDiffusionPipeline
import torch

model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

pipe.load_textual_inversion("sd-concepts-library/cat-toy")

prompt = "A <cat-toy> backpack"

image = pipe(prompt, num_inference_steps=50).images[0]
image.save("cat-backpack.png")
```

</ExampleCodeBlock>

To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first
(for example from [civitAI](https://civitai.com/models/3036?modelVersionId=9857)) and then load the vector
<ExampleCodeBlock anchor="diffusers.StableDiffusionPipeline.load_textual_inversion.example-2">

locally:

```py
from diffusers import StableDiffusionPipeline
import torch

model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")

prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."

image = pipe(prompt, num_inference_steps=50).images[0]
image.save("character.png")
```

</ExampleCodeBlock>



</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>from_single_file</name><anchor>diffusers.StableDiffusionPipeline.from_single_file</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/single_file.py#L271</source><parameters>[{"name": "pretrained_model_link_or_path", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **pretrained_model_link_or_path** (`str` or `os.PathLike`, *optional*) --
  Can be either:
  - A link to the `.ckpt` file (for example
    `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
  - A path to a *file* containing all pipeline weights.
- **torch_dtype** (`str` or `torch.dtype`, *optional*) --
  Override the default `torch.dtype` and load the model with another dtype.
- **force_download** (`bool`, *optional*, defaults to `False`) --
  Whether or not to force the (re-)download of the model weights and configuration files, overriding the
  cached versions if they exist.
- **cache_dir** (`Union[str, os.PathLike]`, *optional*) --
  Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
  is not used.

- **proxies** (`Dict[str, str]`, *optional*) --
  A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
  'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- **local_files_only** (`bool`, *optional*, defaults to `False`) --
  Whether to only load local model weights and configuration files or not. If set to `True`, the model
  won't be downloaded from the Hub.
- **token** (`str` or *bool*, *optional*) --
  The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
  `diffusers-cli login` (stored in `~/.huggingface`) is used.
- **revision** (`str`, *optional*, defaults to `"main"`) --
  The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
  allowed by Git.
- **original_config_file** (`str`, *optional*) --
  The path to the original config file that was used to train the model. If not provided, the config file
  will be inferred from the checkpoint file.
- **config** (`str`, *optional*) --
  Can be either:
  - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
    hosted on the Hub.
  - A path to a *directory* (for example `./my_pipeline_directory/`) containing the pipeline
    component configs in Diffusers format.
- **disable_mmap** ('bool', *optional*, defaults to 'False') --
  Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
  is on a network mount or hard drive.
- **kwargs** (remaining dictionary of keyword arguments, *optional*) --
  Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
  class). The overwritten components are passed directly to the pipelines `__init__` method. See example
  below for more information.</paramsdesc><paramgroups>0</paramgroups></docstring>

Instantiate a [DiffusionPipeline](/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline) from pretrained pipeline weights saved in the `.ckpt` or `.safetensors`
format. The pipeline is set in evaluation mode (`model.eval()`) by default.



<ExampleCodeBlock anchor="diffusers.StableDiffusionPipeline.from_single_file.example">

Examples:

```py
>>> from diffusers import StableDiffusionPipeline

>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = StableDiffusionPipeline.from_single_file(
...     "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
... )

>>> # Download pipeline from local file
>>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt
>>> pipeline = StableDiffusionPipeline.from_single_file("./v1-5-pruned-emaonly.ckpt")

>>> # Enable float16 and move to GPU
>>> pipeline = StableDiffusionPipeline.from_single_file(
...     "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt",
...     torch_dtype=torch.float16,
... )
>>> pipeline.to("cuda")
```

</ExampleCodeBlock>



</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>load_lora_weights</name><anchor>diffusers.StableDiffusionPipeline.load_lora_weights</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L138</source><parameters>[{"name": "pretrained_model_name_or_path_or_dict", "val": ": typing.Union[str, typing.Dict[str, torch.Tensor]]"}, {"name": "adapter_name", "val": ": typing.Optional[str] = None"}, {"name": "hotswap", "val": ": bool = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **pretrained_model_name_or_path_or_dict** (`str` or `os.PathLike` or `dict`) --
  See [lora_state_dict()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict).
- **adapter_name** (`str`, *optional*) --
  Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
  `default_{i}` where i is the total number of adapters being loaded.
- **low_cpu_mem_usage** (`bool`, *optional*) --
  Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
  weights.
- **hotswap** (`bool`, *optional*) --
  Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
  in-place. This means that, instead of loading an additional adapter, this will take the existing
  adapter weights and replace them with the weights of the new adapter. This can be faster and more
  memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
  torch.compile, loading the new adapter does not require recompilation of the model. When using
  hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.

  If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
  to call an additional method before loading the adapter:

```py
pipeline = ...  # load diffusers pipeline
max_rank = ...  # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```

  Note that hotswapping adapters of the text encoder is not yet supported. There are some further
  limitations to this technique, which are documented here:
  https://huggingface.co/docs/peft/main/en/package_reference/hotswap
- **kwargs** (`dict`, *optional*) --
  See [lora_state_dict()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict).</paramsdesc><paramgroups>0</paramgroups></docstring>
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
`self.text_encoder`.

All kwargs are forwarded to `self.lora_state_dict`.

See [lora_state_dict()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict) for more details on how the state dict is
loaded.

See [load_lora_into_unet()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet) for more details on how the state dict is
loaded into `self.unet`.

See [load_lora_into_text_encoder()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder) for more details on how the state
dict is loaded into `self.text_encoder`.




</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>save_lora_weights</name><anchor>diffusers.StableDiffusionPipeline.save_lora_weights</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L469</source><parameters>[{"name": "save_directory", "val": ": typing.Union[str, os.PathLike]"}, {"name": "unet_lora_layers", "val": ": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"}, {"name": "text_encoder_lora_layers", "val": ": typing.Dict[str, torch.nn.modules.module.Module] = None"}, {"name": "is_main_process", "val": ": bool = True"}, {"name": "weight_name", "val": ": str = None"}, {"name": "save_function", "val": ": typing.Callable = None"}, {"name": "safe_serialization", "val": ": bool = True"}, {"name": "unet_lora_adapter_metadata", "val": " = None"}, {"name": "text_encoder_lora_adapter_metadata", "val": " = None"}]</parameters><paramsdesc>- **save_directory** (`str` or `os.PathLike`) --
  Directory to save LoRA parameters to. Will be created if it doesn't exist.
- **unet_lora_layers** (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) --
  State dict of the LoRA layers corresponding to the `unet`.
- **text_encoder_lora_layers** (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) --
  State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
  encoder LoRA state dict because it comes from 🤗 Transformers.
- **is_main_process** (`bool`, *optional*, defaults to `True`) --
  Whether the process calling this is the main process or not. Useful during distributed training and you
  need to call this function on all processes. In this case, set `is_main_process=True` only on the main
  process to avoid race conditions.
- **save_function** (`Callable`) --
  The function to use to save the state dictionary. Useful during distributed training when you need to
  replace `torch.save` with another method. Can be configured with the environment variable
  `DIFFUSERS_SAVE_MODE`.
- **safe_serialization** (`bool`, *optional*, defaults to `True`) --
  Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
- **unet_lora_adapter_metadata** --
  LoRA adapter metadata associated with the unet to be serialized with the state dict.
- **text_encoder_lora_adapter_metadata** --
  LoRA adapter metadata associated with the text encoder to be serialized with the state dict.</paramsdesc><paramgroups>0</paramgroups></docstring>

Save the LoRA parameters corresponding to the UNet and text encoder.




</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>encode_prompt</name><anchor>diffusers.StableDiffusionPipeline.encode_prompt</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L332</source><parameters>[{"name": "prompt", "val": ""}, {"name": "device", "val": ""}, {"name": "num_images_per_prompt", "val": ""}, {"name": "do_classifier_free_guidance", "val": ""}, {"name": "negative_prompt", "val": " = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "lora_scale", "val": ": typing.Optional[float] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  prompt to be encoded
- **device** -- (`torch.device`):
  torch device
- **num_images_per_prompt** (`int`) --
  number of images that should be generated per prompt
- **do_classifier_free_guidance** (`bool`) --
  whether to use classifier free guidance or not
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
  provided, text embeddings will be generated from `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
  argument.
- **lora_scale** (`float`, *optional*) --
  A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.</paramsdesc><paramgroups>0</paramgroups></docstring>

Encodes the prompt into text encoder hidden states.




</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>get_guidance_scale_embedding</name><anchor>diffusers.StableDiffusionPipeline.get_guidance_scale_embedding</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L717</source><parameters>[{"name": "w", "val": ": Tensor"}, {"name": "embedding_dim", "val": ": int = 512"}, {"name": "dtype", "val": ": dtype = torch.float32"}]</parameters><paramsdesc>- **w** (`torch.Tensor`) --
  Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
- **embedding_dim** (`int`, *optional*, defaults to 512) --
  Dimension of the embeddings to generate.
- **dtype** (`torch.dtype`, *optional*, defaults to `torch.float32`) --
  Data type of the generated embeddings.</paramsdesc><paramgroups>0</paramgroups><rettype>`torch.Tensor`</rettype><retdesc>Embedding vectors with shape `(len(w), embedding_dim)`.</retdesc></docstring>

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298








</div></div>

## StableDiffusionPipelineOutput[[diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput</name><anchor>diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_output.py#L11</source><parameters>[{"name": "images", "val": ": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"}, {"name": "nsfw_content_detected", "val": ": typing.Optional[typing.List[bool]]"}]</parameters><paramsdesc>- **images** (`List[PIL.Image.Image]` or `np.ndarray`) --
  List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
  num_channels)`.
- **nsfw_content_detected** (`List[bool]`) --
  List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
  `None` if safety checking could not be performed.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output class for Stable Diffusion pipelines.




</div>

<EditOnGithub source="https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/stable_diffusion/text2img.md" />